RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Not on the map. Why?
Abstract (arXiv)
Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at hrefhttps://github.com/Abe404/RootPainter3Dthis HTTPS URL.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 76 references that are in the map. First posted on arXiv 22 Jun 2021.
Builds on
18 of 76 references.
References · 18
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 2016Understanding deep learning requires rethinking generalizationcited by 1,674
- 2018Group Normalizationcited by 1,524
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2017Deep Learning is Robust to Massive Label Noisecited by 165
- 2017Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentationcited by 66
- 2019Continuous Adaptation for Interactive Object Segmentation by Learning from Correctionscited by 36
- 2019Automated Design of Deep Learning Methods for Biomedical Image Segmentationcited by 27
- 2018Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networkscited by 14
6 more
- 2018Imperfect Segmentation Labels: How Much Do They Matter?cited by 14
- 2020Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessmentcited by 12
- 2019Segmentation of Roots in Soil with U-Netcited by 5
- 2020On Interactive Machine Learning and the Potential of Cognitive Feedbackcited by 5
- 2020Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CTcited by 2
- 2016Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structurescited by 1
Built on by
Citing papers per year: 2023: 1.
1 paper.